{
  "abstract": "Targeted lung cancer screening saves lives by shifting diagnosis to earlier stages, when curative treatment is more likely to succeed. To improve the efficiency of screening and minimise associated harms, tools have been developed to identify individuals at elevated risk. Most major clinical trials defined eligibility using categorical criteria, typically based on age and cumulative smoking exposure—the method adopted in US guidelines since 2013.1 More recently, personalised risk prediction models have been developed to estimate an individual’s probability of developing lung cancer, with the aim of further enhancing screening performance. These models are now in clinical use in countries including England, Canada and Australia.2",
  "authors": [
    {
      "affiliations": [
        "Division of Immunology, Immunity to Infection and Respiratory Medicine, The University of Manchester Faculty of Biology Medicine and Health, Manchester, UK",
        "North West Lung Centre, Manchester University NHS Foundation Trust Department of Thoracic Oncology, Manchester, UK"
      ],
      "name": "Patrick Goodley"
    },
    {
      "affiliations": [
        "Division of Immunology, Immunity to Infection and Respiratory Medicine, The University of Manchester Faculty of Biology Medicine and Health, Manchester, UK",
        "North West Lung Centre, Manchester University NHS Foundation Trust Department of Thoracic Oncology, Manchester, UK"
      ],
      "name": "Philip A J Crosbie"
    }
  ],
  "title": "Improving lung cancer screening: the role and challenges of risk prediction models",
  "uid": "dadbefa6-8754-51a3-944f-378d24922f8f"
}